Abstract:
For decades iris recognition has been widely studied by the scientific community due to its almost unique and stable patterns. Iris recognition biometric systems apply mathematical pattern-recognition techniques to an iris' image of an individual's eye to extract its feature vector. Comparing the dissimilarities from two feature vectors with an acceptance threshold, the system decides if the two vectors are from the same individual's eye. If applied in a well-controlled environment, iris recognition can achieve outstanding accuracies, however, to accomplish that in non-controlled environments is still a challenge researchers are constantly trying to compensate open issues in this context. In order to better explore the patterns found in the iris, researchers have recently begun using a classification approach using multiple signatures, hoping to improve the algorithm robustness. This work aims to explore the effectiveness and scalability of using multiple signatures with a 2-D Gabor filter in a biometric verification system through iris recognition. This is done using two independent Genetic Algorithms to search for the best parameters to the feature extraction technique and on the acceptance frontier search. The method was evaluated by analyzing the behavior of the Half Total Error Rate (HTER) when the number of partitions varies. The experiments showed that the best result was found with 12 partitions on the iris, reaching 0.21% of HTER.
